Decoding Chemical Origins: Bayesian Inference from NMR Fingerprints

Nuclear Inst; Southampton; On-site; GBP 18,000 - 21,000; Full time

Benefits offered by this job

Funding opportunities

Job summary

The University of Southampton is offering a PhD project to determine the origin of chemical compounds from spectroscopic fingerprints. You will combine NMR data with Bayesian inference and machine learning to decode these signatures for forensic science and chemical security.

Collaboration with Dstl and the Interdisciplinary Centre for Magnetic Resonance will help you develop probabilistic models that handle measurement uncertainty and spectral correlations, with training in Python and

Qualifications

• UK 2:1 honours degree or international equivalent in chemistry, physics, engineering, mathematics, statistics, or closely related disciplines.
• Essential skills: interest in applying statistical and computational techniques to challenging problems in chemical analysis; willingness to work across disciplinary boundaries.
• Beneficial skills: experience in Python programming; statistical modelling; NMR spectroscopy.

Responsibilities

• Develop probabilistic models for NMR data using Bayesian methods.
• Investigate isotopic spectral signatures to attribute origins.
• Collaborate with Dstl and the Interdisciplinary Centre for Magnetic Resonance at Southampton.
• Implement scalable analyses in Python and manage datasets.

Education

UK 2:1 honours degree or international equivalent in chemistry / physics / engineering / mathematics / statistics

interdisciplinary collaboration

statistical modelling

Python programming

data analysis

Tools

Python

Job description

Supervisor: Dr. Christian Bengs

Can we determine where a chemical came from using its spectroscopic fingerprint? Even chemically identical compounds can exhibit subtle spectral differences arising from variations in isotopic composition, providing clues about their origins. In this project, you will combine Nuclear Magnetic Resonance data with advanced Bayesian methods and machine learning to decode these signatures, for applications in forensic science and chemical security.

Determining the origin of chemical compounds is an important challenge in analytical chemistry, with applications in forensic science, chemical security and environmental monitoring. Although chemically identical compounds share the same molecular structure, subtle variations in isotopic composition can reveal clues about their production history. Nuclear Magnetic Resonance (NMR) spectroscopy can detect these differences, but extracting reliable information from complex, noisy data remains challenging.

In collaboration with the Defence Science and Technology Laboratory (Dstl), this project will develop statistical methods that combine physical understanding of NMR spectroscopy with Bayesian inference. Beyond classifying samples, we aim to identify which spectral features distinguish chemical origins, understand how they vary between samples, and quantify the confidence with which unknown compounds can be attributed to particular sources.

You will develop probabilistic models that account for measurement uncertainty, sample-to-sample variability and correlations between spectral features. Using experimental NMR datasets, you will investigate how subtle isotopic signatures can support reliable source attribution. The project will also explore how machine learning could complement Bayesian methods, particularly by making complex analyses more efficient and scalable.

Working at the intersection of chemistry, statistics and data science, you will gain hands‑on experience in advanced data analysis, computational modelling and Python programming. You will have opportunities to collaborate with researchers at Dstl and the Interdisciplinary Centre for Magnetic Resonance at the University of Southampton. Training in statistical methods, programming and NMR interpretation will be tailored to your background, providing valuable skills for research and industry.

Training and guidance will be provided in Python programming, statistical modelling and data analysis, depending on your existing experience and background.

You will also receive support in understanding the fundamentals of NMR spectroscopy and interpreting experimental spectra, so prior expertise in these areas is not required.

Entry requirements

You must have a UK 2:1 honours degree or its international equivalent in one of the following:

• chemistry
• physics
• engineering
• mathematics
• statistics
• or closely related disciplines

Essential skills:

• an interest in applying statistical and computational techniques to challenging problems in chemical analysis
• motivation to work across disciplinary boundaries

Beneficial but not essential skills:

• experience in Python programming
• statistical modelling
• NMR spectroscopy

Fees and funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

Faculty of Engineering and Physical Sciences

If you have a general question, email our Doctoral College ([e-mail]).

Project leader

For an initial conversation, email Dr. Christian Bengs ([e-mail]).

The School of Chemistry & Chemical Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.